The explosive growth of user generated content, along with the increasing number and types of content sources, has made it challenging for users to sort out relevant pieces of information from the massive amounts of content available. In this information age where content is king, few content items live in a vacuum. Documents that express opinions or subject evaluations on a range of topics abound. Knowledge workers (e.g., political analysts, financial investors, economists, and business executives), legal professionals (e.g., clerks, lawyers, and judges), and those reading about other topics (e.g., entertainment, health, sports) have to navigate hundreds if not thousands of documents that have opinions, from which it is often hard to extract the relevant information needed.
This content proliferation, while empowering, has led to a poverty of attention. Users' attention to online content is often distributed in a highly skewed fashion. Most documents receive a negligible amount of attention, while only a few become extremely popular. Users may find it difficult to browse/find the most salient documents in a large corpus of documents and quickly spot relevant opinions in each document to situate them within a “big picture”. Potentially relevant content may therefore be missed when users forage for information among large corpuses of interlinked documents.
Currently available content and document browsing solutions fail to help users allocate their attention effectively among the overabundance of information sources that might consume it. The currently available solutions suffer from critical limitations, including not being able to situate each document's opinion in a broader context and present key opinions in a manner that is natural to users. Many of the existing solutions add to the information overload by presenting the users with more information (e.g., multiple documents side-by-side) in increasingly smaller areas of screen real-estate. Reading documents in parallel may lead to issues of practice inference in working memory, thereby making it even more difficult for users to extract and remember relevant content.